[zero] adapt zero for unsharded paramters (Optimizer part) (#601)

tests/test_moe/test_moe_zero_optim.py

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+from functools import partial
+
+import colossalai
+from colossalai.utils.cuda import get_current_device
+import pytest
+import torch
+import torch.multiprocessing as mp
+from colossalai.amp import convert_to_apex_amp
+from colossalai.nn.optimizer import CPUAdam
+from colossalai.testing import parameterize, rerun_on_exception
+from colossalai.utils import free_port
+from colossalai.zero.init_ctx import ZeroInitContext
+from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
+from colossalai.zero.sharded_model import ShardedModelV2
+from colossalai.zero.sharded_model.utils import col_model_deepcopy
+from colossalai.zero.sharded_optim import ShardedOptimizerV2
+from colossalai.zero.sharded_optim._utils import has_inf_or_nan
+from colossalai.utils import get_current_device
+from tests.components_to_test.registry import non_distributed_component_funcs
+from colossalai.engine.gradient_handler import MoeGradientHandler
+from colossalai.context import MOE_CONTEXT
+from colossalai.testing import assert_equal_in_group
+
+from tests.test_zero_data_parallel.common import CONFIG, check_sharded_model_params
+from tests.test_moe.test_moe_zero_init import MoeModel
+
+
+def _run_step(model, optimizer, data, label, criterion, grad_handler):
+    model.train()
+    optimizer.zero_grad()
+
+    if criterion:
+        y = model(data)
+        loss = criterion(y, label)
+    else:
+        loss = model(data, label)
+
+    loss = loss.float()
+    if isinstance(model, ShardedModelV2):
+        optimizer.backward(loss)
+    else:
+        loss.backward()
+
+    if grad_handler is not None:
+        grad_handler.handle_gradient()
+
+    optimizer.step()
+
+
+@parameterize("cpu_offload", [True, False])
+@parameterize("use_cpuadam", [True, False])
+@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
+def _run_test_sharded_optim_v2(cpu_offload, shard_strategy_class, use_cpuadam, gpu_margin_mem_ratio=0.0):
+    MOE_CONTEXT.reset_loss()
+    shard_strategy = shard_strategy_class()
+    if use_cpuadam and cpu_offload is False:
+        return
+
+    get_components_func = non_distributed_component_funcs.get_callable('no_leaf_module')
+    _, train_dataloader, _, optimizer_class, criterion = get_components_func()
+
+    with ZeroInitContext(
+            target_device=torch.device('cpu') if cpu_offload else torch.device(f'cuda:{get_current_device()}'),
+            shard_strategy=shard_strategy,
+            shard_param=True,
+            rm_torch_payload_on_the_fly=False):
+        zero_model = MoeModel()
+
+    zero_model = ShardedModelV2(
+        zero_model,
+        shard_strategy,
+        offload_config=dict(device='cpu') if cpu_offload else None,
+        use_memory_tracer=gpu_margin_mem_ratio > 0.0,
+        reuse_fp16_shard=use_cpuadam,
+    )
+
+    # check whether parameters are identical in ddp
+    for name, p in zero_model.named_parameters():
+        if not p.colo_attr.param_is_sharded and p.is_replicated:
+            assert_equal_in_group(p.data.to(get_current_device()))
+
+    model = MoeModel().half()
+    col_model_deepcopy(zero_model, model)
+    model = model.cuda().float()
+
+    if use_cpuadam:
+        optimizer_class = CPUAdam
+    optim = optimizer_class(model.parameters(), lr=1e-3)
+    sharded_optim = optimizer_class(zero_model.parameters(), lr=1e-3)
+    sharded_optim = ShardedOptimizerV2(zero_model,
+                                       sharded_optim,
+                                       cpu_offload=cpu_offload,
+                                       initial_scale=2**5,
+                                       gpu_margin_mem_ratio=gpu_margin_mem_ratio,
+                                       keep_unsharded=True)
+
+    amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False)
+    apex_model, apex_optimizer = convert_to_apex_amp(model, optim, amp_config)
+    apex_grad_handler = MoeGradientHandler(model)
+
+    # Since MOE is not compatible with apex_amp now, we need to convert gate weight to fp32
+    for (n, p), zp in zip(apex_model.named_parameters(), zero_model.parameters()):
+        if 'gate' in n:
+            p.data = p.float()
+            p.data.copy_(zp.data)
+
+    for i, (data, label) in enumerate(train_dataloader):
+        if i > 5:
+            break
+        data, label = data.cuda(), label.cuda()
+        _run_step(apex_model, apex_optimizer, data, label, criterion, apex_grad_handler)
+        _run_step(zero_model, sharded_optim, data, label, criterion, None)
+        check_sharded_model_params(model, zero_model, loose=True, reuse_fp16_shard=use_cpuadam)
+        for param in model.parameters():
+            assert not has_inf_or_nan(param)
+
+
+def _run_dist(rank, world_size, port):
+    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    MOE_CONTEXT.setup(seed=42)
+    _run_test_sharded_optim_v2()
+
+
+# use_cpuadam = True can be used with cpu_offload = False
+@pytest.mark.dist
+@pytest.mark.parametrize("world_size", [2])
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
+def test_moe_zero_optim(world_size):
+    run_func = partial(_run_dist, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_moe_zero_optim(world_size=2)